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  <h1>Source code for torchvision.ops.boxes</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch.jit.annotations</span> <span class="kn">import</span> <span class="n">Tuple</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>
<span class="kn">import</span> <span class="nn">torchvision</span>


<div class="viewcode-block" id="nms"><a class="viewcode-back" href="../../../ops.html#torchvision.ops.nms">[docs]</a><span class="k">def</span> <span class="nf">nms</span><span class="p">(</span><span class="n">boxes</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="n">iou_threshold</span><span class="p">):</span>
    <span class="c1"># type: (Tensor, Tensor, float)</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Performs non-maximum suppression (NMS) on the boxes according</span>
<span class="sd">    to their intersection-over-union (IoU).</span>

<span class="sd">    NMS iteratively removes lower scoring boxes which have an</span>
<span class="sd">    IoU greater than iou_threshold with another (higher scoring)</span>
<span class="sd">    box.</span>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    boxes : Tensor[N, 4])</span>
<span class="sd">        boxes to perform NMS on. They</span>
<span class="sd">        are expected to be in (x1, y1, x2, y2) format</span>
<span class="sd">    scores : Tensor[N]</span>
<span class="sd">        scores for each one of the boxes</span>
<span class="sd">    iou_threshold : float</span>
<span class="sd">        discards all overlapping</span>
<span class="sd">        boxes with IoU &gt; iou_threshold</span>

<span class="sd">    Returns</span>
<span class="sd">    -------</span>
<span class="sd">    keep : Tensor</span>
<span class="sd">        int64 tensor with the indices</span>
<span class="sd">        of the elements that have been kept</span>
<span class="sd">        by NMS, sorted in decreasing order of scores</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">torchvision</span><span class="o">.</span><span class="n">nms</span><span class="p">(</span><span class="n">boxes</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="n">iou_threshold</span><span class="p">)</span></div>


<span class="k">def</span> <span class="nf">batched_nms</span><span class="p">(</span><span class="n">boxes</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="n">idxs</span><span class="p">,</span> <span class="n">iou_threshold</span><span class="p">):</span>
    <span class="c1"># type: (Tensor, Tensor, Tensor, float)</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Performs non-maximum suppression in a batched fashion.</span>

<span class="sd">    Each index value correspond to a category, and NMS</span>
<span class="sd">    will not be applied between elements of different categories.</span>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    boxes : Tensor[N, 4]</span>
<span class="sd">        boxes where NMS will be performed. They</span>
<span class="sd">        are expected to be in (x1, y1, x2, y2) format</span>
<span class="sd">    scores : Tensor[N]</span>
<span class="sd">        scores for each one of the boxes</span>
<span class="sd">    idxs : Tensor[N]</span>
<span class="sd">        indices of the categories for each one of the boxes.</span>
<span class="sd">    iou_threshold : float</span>
<span class="sd">        discards all overlapping boxes</span>
<span class="sd">        with IoU &gt; iou_threshold</span>

<span class="sd">    Returns</span>
<span class="sd">    -------</span>
<span class="sd">    keep : Tensor</span>
<span class="sd">        int64 tensor with the indices of</span>
<span class="sd">        the elements that have been kept by NMS, sorted</span>
<span class="sd">        in decreasing order of scores</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="n">boxes</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">0</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">boxes</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
    <span class="c1"># strategy: in order to perform NMS independently per class.</span>
    <span class="c1"># we add an offset to all the boxes. The offset is dependent</span>
    <span class="c1"># only on the class idx, and is large enough so that boxes</span>
    <span class="c1"># from different classes do not overlap</span>
    <span class="n">max_coordinate</span> <span class="o">=</span> <span class="n">boxes</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
    <span class="n">offsets</span> <span class="o">=</span> <span class="n">idxs</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">boxes</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">max_coordinate</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">boxes_for_nms</span> <span class="o">=</span> <span class="n">boxes</span> <span class="o">+</span> <span class="n">offsets</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span>
    <span class="n">keep</span> <span class="o">=</span> <span class="n">nms</span><span class="p">(</span><span class="n">boxes_for_nms</span><span class="p">,</span> <span class="n">scores</span><span class="p">,</span> <span class="n">iou_threshold</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">keep</span>


<span class="k">def</span> <span class="nf">remove_small_boxes</span><span class="p">(</span><span class="n">boxes</span><span class="p">,</span> <span class="n">min_size</span><span class="p">):</span>
    <span class="c1"># type: (Tensor, float)</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Remove boxes which contains at least one side smaller than min_size.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        boxes (Tensor[N, 4]): boxes in (x1, y1, x2, y2) format</span>
<span class="sd">        min_size (float): minimum size</span>

<span class="sd">    Returns:</span>
<span class="sd">        keep (Tensor[K]): indices of the boxes that have both sides</span>
<span class="sd">            larger than min_size</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">ws</span><span class="p">,</span> <span class="n">hs</span> <span class="o">=</span> <span class="n">boxes</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="n">boxes</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">boxes</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">-</span> <span class="n">boxes</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span>
    <span class="n">keep</span> <span class="o">=</span> <span class="p">(</span><span class="n">ws</span> <span class="o">&gt;=</span> <span class="n">min_size</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">hs</span> <span class="o">&gt;=</span> <span class="n">min_size</span><span class="p">)</span>
    <span class="n">keep</span> <span class="o">=</span> <span class="n">keep</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">keep</span>


<span class="k">def</span> <span class="nf">clip_boxes_to_image</span><span class="p">(</span><span class="n">boxes</span><span class="p">,</span> <span class="n">size</span><span class="p">):</span>
    <span class="c1"># type: (Tensor, Tuple[int, int])</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Clip boxes so that they lie inside an image of size `size`.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        boxes (Tensor[N, 4]): boxes in (x1, y1, x2, y2) format</span>
<span class="sd">        size (Tuple[height, width]): size of the image</span>

<span class="sd">    Returns:</span>
<span class="sd">        clipped_boxes (Tensor[N, 4])</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">dim</span> <span class="o">=</span> <span class="n">boxes</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span>
    <span class="n">boxes_x</span> <span class="o">=</span> <span class="n">boxes</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">::</span><span class="mi">2</span><span class="p">]</span>
    <span class="n">boxes_y</span> <span class="o">=</span> <span class="n">boxes</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">::</span><span class="mi">2</span><span class="p">]</span>
    <span class="n">height</span><span class="p">,</span> <span class="n">width</span> <span class="o">=</span> <span class="n">size</span>

    <span class="k">if</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">_is_tracing</span><span class="p">():</span>
        <span class="n">boxes_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">boxes_x</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">boxes</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">boxes</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>
        <span class="n">boxes_x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">boxes_x</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">width</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">boxes</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">boxes</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>
        <span class="n">boxes_y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">boxes_y</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">boxes</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">boxes</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>
        <span class="n">boxes_y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">boxes_y</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">height</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">boxes</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">boxes</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="n">boxes_x</span> <span class="o">=</span> <span class="n">boxes_x</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="n">width</span><span class="p">)</span>
        <span class="n">boxes_y</span> <span class="o">=</span> <span class="n">boxes_y</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="n">height</span><span class="p">)</span>

    <span class="n">clipped_boxes</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">((</span><span class="n">boxes_x</span><span class="p">,</span> <span class="n">boxes_y</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">clipped_boxes</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">boxes</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">box_area</span><span class="p">(</span><span class="n">boxes</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Computes the area of a set of bounding boxes, which are specified by its</span>
<span class="sd">    (x1, y1, x2, y2) coordinates.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        boxes (Tensor[N, 4]): boxes for which the area will be computed. They</span>
<span class="sd">            are expected to be in (x1, y1, x2, y2) format</span>

<span class="sd">    Returns:</span>
<span class="sd">        area (Tensor[N]): area for each box</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="p">(</span><span class="n">boxes</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="n">boxes</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">*</span> <span class="p">(</span><span class="n">boxes</span><span class="p">[:,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">-</span> <span class="n">boxes</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">])</span>


<span class="c1"># implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py</span>
<span class="c1"># with slight modifications</span>
<span class="k">def</span> <span class="nf">box_iou</span><span class="p">(</span><span class="n">boxes1</span><span class="p">,</span> <span class="n">boxes2</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Return intersection-over-union (Jaccard index) of boxes.</span>

<span class="sd">    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.</span>

<span class="sd">    Arguments:</span>
<span class="sd">        boxes1 (Tensor[N, 4])</span>
<span class="sd">        boxes2 (Tensor[M, 4])</span>

<span class="sd">    Returns:</span>
<span class="sd">        iou (Tensor[N, M]): the NxM matrix containing the pairwise</span>
<span class="sd">            IoU values for every element in boxes1 and boxes2</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">area1</span> <span class="o">=</span> <span class="n">box_area</span><span class="p">(</span><span class="n">boxes1</span><span class="p">)</span>
    <span class="n">area2</span> <span class="o">=</span> <span class="n">box_area</span><span class="p">(</span><span class="n">boxes2</span><span class="p">)</span>

    <span class="n">lt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">boxes1</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">,</span> <span class="p">:</span><span class="mi">2</span><span class="p">],</span> <span class="n">boxes2</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">])</span>  <span class="c1"># [N,M,2]</span>
    <span class="n">rb</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">boxes1</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">2</span><span class="p">:],</span> <span class="n">boxes2</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">:])</span>  <span class="c1"># [N,M,2]</span>

    <span class="n">wh</span> <span class="o">=</span> <span class="p">(</span><span class="n">rb</span> <span class="o">-</span> <span class="n">lt</span><span class="p">)</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>  <span class="c1"># [N,M,2]</span>
    <span class="n">inter</span> <span class="o">=</span> <span class="n">wh</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">wh</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span>  <span class="c1"># [N,M]</span>

    <span class="n">iou</span> <span class="o">=</span> <span class="n">inter</span> <span class="o">/</span> <span class="p">(</span><span class="n">area1</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">+</span> <span class="n">area2</span> <span class="o">-</span> <span class="n">inter</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">iou</span>
</pre></div>

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